Changes in substance supply and use characteristics among people who use drugs (PWUD) during the COVID-19 global pandemic: A national qualitative assessment in Canada
Bibliographic record
Abstract
BACKGROUND: People who use drugs (PWUD) may be at an increased risk of experiencing negative effects related to COVID-19. Border and non-essential service closures may have placed PWUD at an increased risk of experiencing unintended consequences regarding drug consumption and supply patterns, as well as related outcomes. However, the extent of these effects upon this population is unknown. The current study examined how COVID-19 has impacted substance use supply and use characteristics among a national cohort of PWUD in Canada. METHODS: We conducted semi-structured one-on-one telephone-based interviews with 200 adult PWUD across Canada who were currently using a licit or illicit psychoactive substance at least weekly, and/or currently receiving opioid agonist treatment (OAT). Thematic analyses were conducted using qualitative software. RESULTS: PWUD attributed adverse changes to their substance use frequency, supply, use patterns, and risk behaviors and outcomes to COVID-19. Many participants noted supply disruptions with the majority indicating a decrease in potency and availability, and an increase in the price of substances since COVID-19. Nearly half of participants specified that they had increased their substance use, with some experiencing relapses. In terms of changes to risk level, many participants perceived they were at a greater risk for experiencing an overdose. CONCLUSION: This study demonstrated the impacts of COVID-19 on PWUD, including a significant disruption substance supply. For many, these changes led to increased use and substitution for toxic and adulterated substances, which ultimately amplified PWUD's risk for experiencing related harms, including overdoses. These findings warrant the need for improved supports and services, as well as accessibility of safe supply programs, take home naloxone kits, and novel approaches to ensure PWUD have the tools necessary to mitigate risk when using substances.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".